Statistical Principles Behind Credit Scoring

Business Knowledge Series course

This course explains the statistical principles that underlie the methods in credit risk scoring. It discusses how to interpret statistical and other relevant computations in credit risk modeling. Participants will learn how segmentation may be used to improve credit scoring initiatives. Initial variable analysis is introduced, and predictive models in credit risk are explained. The course clarifies the formulas for the accuracy and validation measures used as well as the quantities generated in scorecard reports. The concept of reject inference is discussed, and the possible approaches to implement it are presented.

Learn how to

• understand and interpret the statistical concepts in risk analytics
• prepare data for credit risk analysis and perform exploratory data analysis
• use segmentation for credit risk modeling
• perform initial variable analysis
• interpret predictive models for credit risk as well as accuracy and validation measures
• perform reject inference and understand quantities in scorecard reports

Who should attend

Those involved or would be involved in credit risk scoring may attend this course.

Formats available	Duration
Classroom:	1 day

There are no prerequisites for this course.

Course Contents

• Statistical Principles in Risk Analytics
• Data Preparation and Exploration for Credit Risk Modeling
• Segmentation in Credit Risk Analytics
• Initial Variable Analysis
• Predictive Models for Credit Scoring
• Accuracy and Validation Measures
• Reject Inference
• Statistical Quantities in Scorecard Reports